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bge-multilingual-gemma2 Cost Calculator - Nebius

Calculate the cost of using bge-multilingual-gemma2 from Nebius for your AI applications

bge-multilingual-gemma2 Cost Calculator

Mode: Embedding

Max: 8,192 tokens

Cost Breakdown

Input Cost$0.00001000
Total Cost$0.00001000

Pricing Details

Input: $0.0000000100 per token
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Model Specifications

Limits

Max Input Tokens8,192
Max Tokens8,192

About bge-multilingual-gemma2

bge-multilingual-gemma2 is a powerful embedding AI model offered by Nebius. This comprehensive guide provides detailed pricing information, technical specifications, and capabilities to help you understand the costs and features of using bge-multilingual-gemma2 in your embedding applications.

Pricing Information

Input Cost$0.01 per 1M tokens

Note: Use the interactive calculator above to estimate costs for your specific usage patterns.

Technical Specifications

Maximum Input Tokens8,192
Maximum Total Tokens8,192

Pro Tip

Use the maximum token limits shown above to understand the model's capacity. This model can handle up to 8,192 input tokens.

When should you use bge-multilingual-gemma2?

bge-multilingual-gemma2 is best suited for the following scenarios:

  • Semantic search and similarity matching
  • RAG pipelines and vector databases
  • Document clustering and recommendations
When should you avoid bge-multilingual-gemma2?
  • Complex multi-step reasoning or planning tasks
  • Applications requiring image, audio, or multimodal inputs
  • Very large documents or long conversational histories
  • General-purpose text generation or conversational AI
  • Creative writing or content generation tasks
How does bge-multilingual-gemma2 compare to similar models?

This model offers competitive input token pricing, making it cost-effective for applications that require extensive context or frequent input processing.

Understanding bge-multilingual-gemma2 pricing
  • bge-multilingual-gemma2 is a embedding and vector search model provided by Nebius.
  • Input tokens are priced at $0.01 per 1M tokens.
  • The model supports a maximum input capacity of 8,192 tokens.
  • Nebius offers bge-multilingual-gemma2 for embedding and vector search workloads — semantic search, similarity matching, recommendation systems, and vector-based applications.

How to Use This Calculator

Step 1: Enter the number of input tokens you expect to use. Input tokens include your prompt, system messages, and any context you provide to the model.

Step 2: Specify the number of output tokens you anticipate. Output tokens are the text generated by the model in response to your input.

Step 3: Review the cost breakdown to see the total estimated cost for your usage. The calculator automatically updates as you adjust the token counts.